Due to the real-time nature and the value of social media content for monitoring entities and events of significance, automated sentiment analysis and semantic enrichment techniques for social media streams have received considerable attention in the literature. These techniques are central to monitoring social-media content, which is now becoming a significant business with commercial, institutional, governmental and law enforcement interest into its applications. Prior work in sentiment analysis especially has focused mostly on negative-positive sentiment classification tasks. Although numerous approaches employ highly elaborate and effective techniques with some success, the sentiment or emotion granularity is generally limiting and arguably not always most appropriate for real-world problems. In this paper a newly developed ontology based system is employed, to semantically enrich Tweets with fine-grained emotional states, in order to analyse the subjective public reactions to a wide selection of recent events. The approach detects a range of eight high-level emotions and their perceived strength (also known as activation level), specifically; anger, confusion, disgust, fear, happiness, sadness, shame and surprise. A set of emotional profiles for different events is obtained and an in-depth analysis of the emotional responses is presented. Recent events, such as the 2013 horsemeat scandal, Nelson Mandela’s death, September 11th remembrance anniversary, recent tube strikes in London are analysed and discussed. The feasibility and potential benefits of automated fine-grained emotional event response analysis from social-media is illustrated and further, future work suggested.